The objective of the proposed research is to develop multivariate statistical methods for joint analyses of functionally related biological information n association and interaction using existing GWA data. Specifically, we propose to develop methods for joint analyzing multiple SNPs in a SNP set (e.g. a gene), and methods for jointly analyzing multivariate secondary phenotypes with potential ignorable and non-ignorable missing data. We further propose to develop methods for genome-wide gene-gene interaction analysis, in which groups of between- gene SNP-SNP correlations will be analyzed together to detect interactions. In addition, we propose to integrate a priori knowledge in our genome-wide association or interaction analyses; and we group sets of predictors by a priori knowledge and design flexible regularized regression approaches to constrain the parameter estimation and achieve efficiency. The proposed research is motivated by opportunities and needs in GWA studies, and much of our proposed work can be cost-efficiently implemented with publicly accessible GWA data for different cancers to improve our understanding of the genetic basis and disease etiology of cancer.